Multi-Stage Generative Upscaler: Reconstructing Football Broadcast Images via Diffusion Models

Generative Artificial Intelligence (genAI) represents a groundbreaking approach to creativity and automation, empowering machines to produce novel and highly realistic data, including images, text, and music. Among the diverse generative models, Diffusion Models have emerged as a powerful technique for high-quality image synthesis. Rooted in the principles of probabilistic modeling, Diffusion Models iteratively refine noise into detailed and coherent representations, achieving remarkable performance in domains like image generation, image inpainting and style transfer. Diffusion Models have gained traction due to their versatility and robustness, allowing them to excel in challenging tasks where conventional generative approaches, such as Generative Adversarial Networks (GANs), often struggle. These models leverage a forward-backward diffusion process, where images are progressively noised during the forward phase and restored to their original form during the reverse phase.